Modern electronic and mechanical systems, such as aircraft or other vehicles, are becoming increasingly complex. Health management systems are often used to monitor various health states of vehicle systems. Several factors contribute to the evolution of the health states. These factors include damage accumulation, interaction between components in the system, deviation from design characteristics, and the influence of continuous or discrete events. These aspects can be modeled and evaluated using prognostic and diagnostic indictors in an effort to predict faults in the vehicle system. However, the complexities of modern electronic and mechanical systems have led to increasing needs for more sophisticated health systems. Information about potential faults enables such faults to be addressed before issues arise. In general, this information may provide support for an operator or other individual for use in making decisions regarding future maintenance, operation, or use of the system, and/or for use in making other decisions.
Some health management systems that have been developed are configurable, in the sense that these health management systems can be deployed across various applications. Such health management systems typically rely on a library of algorithms. Each of the algorithms within the library includes one or more associated parameters that may need to be customized for the application in which it is deployed. Some of these parameters, such as pipe diameter, are readily obtainable from asset/system specifications or measurements. However, various other parameters, such as all data driven parameters or certain model parameters, may not be readily available, but need to be selected prior to system/asset deployment. These latter parameters may be selected by specialized personnel, such as an algorithm expert, based on their knowledge of the particular asset/system and a review of associated asset/system historic data. As may be appreciated, this latter parameter selection process may involve trial and error, can be relatively time consuming, is potentially prone to suboptimal selection of parameters, and relies on specialized personnel for parameter selection. Optimization of parameters generally relies on an optimization expert to formulate an appropriate objective function for a specific optimization problem. This invention makes parameter optimization for diagnostic/prognostic problem usable to non-experts by defining a generic objective function for all detection problems of this sort.
Hence, there is a need for process of selecting parameters for diagnostic algorithms within a library of diagnostic algorithms that does not rely on specialized personnel, and thus does not involve human-effected trial and error, and/or is relatively less time consuming than current methods, and/or is less prone to suboptimal selection. The present invention addresses at least these needs.